The less is more linear algebra of vector spaces and matrices

著者

書誌事項

The less is more linear algebra of vector spaces and matrices

Daniela Calvetti, Erkki Somersalo

(OT, 187)

Society for Industrial and Applied Mathematics, c2023

大学図書館所蔵 件 / 1

この図書・雑誌をさがす

内容説明・目次

内容説明

Designed for a proof-based course on linear algebra, this rigorous and concise textbook intentionally introduces vector spaces, inner products, and vector and matrix norms before Gaussian elimination and eigenvalues so students can quickly discover the singular value decomposition (SVD)-arguably the most enlightening and useful of all matrix factorizations. Gaussian elimination is then introduced after the SVD and the four fundamental subspaces and is presented in the context of vector spaces rather than as a computational recipe. This allows the authors to use linear independence, spanning sets and bases, and the four fundamental subspaces to explain and exploit Gaussian elimination and the LU factorization, as well as the solution of overdetermined linear systems in the least squares sense and eigenvalues and eigenvectors. This unique textbook also includes examples and problems focused on concepts rather than the mechanics of linear algebra. The problems at the end of each chapter and in an associated website encourage readers to explore how to use the notions introduced in the chapter in a variety of ways. Additional problems, quizzes, and exams will be posted on an accompanying website and updated regularly. The Less Is More Linear Algebra of Vector Spaces and Matrices is for students and researchers interested in learning linear algebra who have the mathematical maturity to appreciate abstract concepts that generalize intuitive ideas. The early introduction of the SVD makes the book particularly useful for those interested in using linear algebra in applications such as scientific computing and data science. It is appropriate for a first proof-based course in linear algebra.

「Nielsen BookData」 より

関連文献: 1件中  1-1を表示

  • OT

    Society for Industrial and Applied Mathematics

詳細情報

  • NII書誌ID(NCID)
    BD02367996
  • ISBN
    • 9781611977394
  • LCCN
    2022948985
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Philadelphia
  • ページ数/冊数
    xii, 168 p.
  • 大きさ
    26 cm
  • 分類
  • 件名
  • 親書誌ID
ページトップへ